This class put more emphases on applications instead of statisical theories. Upon completion of the class, students are expected to be able to:
Grading: Four sets of homework, each worth 15%. Final project 30%. Class participate 10%.
Lab: Bring laptop to the labs.
Here are some reading materials related to the class. It is NOT required to read them all, but it'll help you get better understanding of certain materials.
The final project could be (but not limited to) exploratory analysis, statistical modeling, or analytical software development for any type of genomic data. Some ideas of projects include:
Requirement for the report
Students need to submit a short report for the final project.
The report needs to be a single docx or pdf file with file name as NAME_BIOS555_finalproject.docx.
Here NAME is your full name. The R codes should be submitted as appendix,
but need to be in the same file.
The report should contain following sections:
Submit the report
The report is due on Oct 22, 2021 at midnight, four days after the class ends.
Please send the report (as a single attachment) to the instructor and cc the TA.
NOTE: Due to the policy change in 2021, I MUST enter the students' grades in OPUS by 10/26. So I will not take project not submitted by the deadline. I appreciate if you can submit the project earlier, which will relieve my stress to grade 20+ final projects in a few days.
|Date||Lecture Title||Description||Homework||Suggested Reading|
|8/25 (Wed)||Lecture 1: Introduction [PDF]||Brief introduction of molecular biology, high-throughput experiments, R and Bioconductor.||Wikipedia pages for gene, genome, microarray and sequencing.||8/30 (Mon)||Lab 1: Simple genomic analysis using R [PDF, R]||Exploratory analysis of human refseq genes.||Homework1||9/1 (Wed)||Lecture 2: Gene expression microarray I [PDF]||Experimental procedures and data pre-processing methods for Gene expression microarrays.||The microarray review article, RMA and GCRMA papers.||9/6 (Mon)||Labor day, no class||9/8 (Wed)||Lecture 3: Gene expression microarray II, tiling arrays. [PDF]||Differential expression from GE arrays. Biological and technical artifacts: batch effects, cell type mixture..||SAM and Limma papers.||9/13 (Mon)||Lab 2: Analyzing gene expression array data from MAQC. [PDF][R]||Using gene expression microarrays generated by MAQC project, we will explore data produced from different array designs and compare to the gold standard. The gold standard Taqman data can be found at here.||Homework2||9/15 (Wed)||Lecture 4: Handling genome data using Bioconductor I [PDF][R]||Introduce Biostrings and BSgenome Bioconductor packages.||PLoS CB paper, Package Vignettes for Biostrings and BSgenome.||9/29 (Mon)||Lecture 5: Handling genome data using Bioconductor II [PDF][R]||Introduce GenomicRanges and GenomicFeatures Bioconductor packages.||PLoS CB paper, Package Vignettes for GenomicRanges and GenomicFeatures.||9/22 (Wed)||Lab 3: Analyzing human genome [PDF][R]||Study the sequence composition of human genome. Look at overlaps of CpG islands and gene promoters.||Homework3||9/27 (Mon)||Lecture 6: Introduction to second generation sequencing [PDF]||Introduce second generation sequencing technologies and software for alignment, variant calling and visualization.||9/29 (Wed)||Lecture 7: RNA-seq [PDF]||Experimental procedure and data analysis for RNA-seq data. Normalization and differential expression detection. DEseq and edgeR Bioconductor package.||edgeR, DESeq, and cufflink papers||10/4 (Mon)||Lecture 8: ChIP-seq [PDF]||Experimental procedure of ChIP-seq. Peak calling methods. Comparison of multiple ChIP-seq. Joint analysis of ChIP-seq and RNA-seq.||MACS and PeakSeq papers||10/6 (Wed)||Lab 4: Handling second generation sequencing data, RNA- and ChIP-seq analyses [PDF][R]||This lab will practice the materials covered in three lectures. (1) sequence alignment and manipulation. (2) Joint analysis of ChIP-seq and RNA-seq. (3) RNA-seq analysis using DEseq and edgeR.||Homework4||10/11 (Mon)||Fall break, no class||10/13 (Wed)||Lecture 9: Bisulfite sequencing [PDF]||Experimental procedure of bisulfite sequencing. Differential methylation. DNA methylation and protein binding.||BSmooth and DSS papers||10/18 (Mon)||Lecture 10: Single-cell sequencing [PDF]||Briefly introduce single-cell sequencing technologies and data analysis, with emphasis on single-cell RNA sequencing (scRNA-seq).||Monocle, Wanderlust, RaceID, and MAST papers|